导　　师： 雷秀娟

学科专业： 081203

授予学位： 硕士

作　　者： ;

机构地区： 陕西师范大学

摘　　要： 优化问题已贯穿于社会的各个领域。最初人们都是采用传统的优化方法来解决工程应用中的相关优化问题,随着所要解决的优化问题复杂性的不断增加,传统方法己不能满足需求。在20世纪80年代出现的群智能算法弥补了传统优化方法的缺陷。在群智能算法中,遗传算法/(Genetic Algorithm, GA/)、蚁群优化/(AntColony Optimization, ACO/)算法、粒子群优化/(Particle Swarm Optimization, PSO/)算法以及人工鱼群/(Artificial Fish Swarm, AFS/)算法都是从高等生物中得到启发而产生的算法,而细菌觅食优化/(Bacteria Foraging Optimization, BFO/)算法是20世纪末相关学者通过模拟微生物寻找食物源的行为所形成的一种较新的优化方法。 聚类分析是一种很重要的数据挖掘技术,它能高效地从一堆数据中提炼出人们所需要的有价值的信息。本文详细介绍了聚类算法的基本原理及其相关基础知识,重点介绍了K-means聚类算法,本文的研究工作主要包括BFO算法和K-means聚类算法的融合以及对BFO算法自身进行改进两方面,具体如下： 以改进K-means为出发点,提出了基于BFO算法的K-means聚类算法——K-BFO,其基本思想是：利用BFO算法的全局搜索性,确定初始聚类中心,从而避免了K-means算法随机选取初始聚类中心的弊端。该算法具有四个优点：/(1/)对包含不同类别大小的数据集不敏感；/(2/)新算法具有并行性的特点,其收敛速度快；/(3/)算法可用来处理高维数据聚类的问题；/(4/)与基于PSO的聚类算法相比较,K-BFO算法过程简单,易于理解,并能取得更好的聚类效果。 改进BFO算法。具体包括三方面工作：/(1/)标准BFO算法的趋向性操作中翻转方向的随机性会直接影响算法的寻优速度,对趋向性操作中随机的选择方向进行改进：让个体向最优方向的细菌学习,若当前没有最好则随机选择方向。/(2/)标准BFO算法复制操作中,适� Optimization problem has been widely used in different areas. Traditional optimization methods are originally adopted to solve optimization problems in engineering applications. Swarm intelligent algorithms were proposed in the1980s to make up the shortcomings of traditional algorithms which cannot address optimization problems with the increasing of complexity. Among all swarm intelligent algorithms, genetic algorithm/(GA/), ant colony optimization/(ACO/) algorithm, particle swarm optimization/(PSO/) algorithm and artificial fish swarm/(AFS/) algorithm are inspired by higher living creatures, while bacteria foraging optimization/(BFO/) algorithm is a novel optimization algorithm proposed in2002by mimicking foraging behavior of microorganism searching for food source. Clustering is a significant data mining technology, which is capable of efficiently extracting valuable information needed. The basic principles and concepts of clustering are introduced in detail in this dissertation, especially K-means clustering method. The main researches of this paper refer to the improvement of BFO algorithm and combination of BFO algorithm and K-means clustering method, as follows: Aimed at improving the K-means method, modified K-means method based on BFO was proposed in this paper, namely K-BFO algorithm, in which the basic idea is taking advantages of global searching abilities of BFO algorithm to determine the initial cluster center for avoiding the weakness of choosing the initial cluster center randomly in K-means algorithm. There are three advantages of the novel algorithm:/(1/) Insensitivity to datasets with various kinds and sizes of data;/(2/) Characteristic of concurrency for high convergence speed;/(3/) Dealing with clustering of data in high dimensions;/(4/) By comparison with PSO based clustering algorithm, K-BFO algorithm possesses simply process for easy understanding, which can obtain better clustering results. The BFO algorithm is improved in this paper, includes three aspects:/(1/) To overcome the

关 键 词： 群智能算法 细菌觅食优化算法 聚类分析 聚类算法

领　　域： [自动化与计算机技术] [自动化与计算机技术]